Catalysing effective social accountability systems through community participation
Bibliographic record
Abstract
Worldwide, infrastructure expansion and visions of ‘slum-free cities’ displace people living in informal settlements. Without community participation in these processes and accountability mechanisms in place’ such displacement can adversely impact people’s health and well-being. This piece outlines SPARC’s (Society For Promotion of Area Resource Centres, SPARC is an NGO based in India promoting action of organised communities of urban poor to negotiate with the state on accessing tenure security, housing, sanitation and civic services) experience promoting community participation among residents of a relocation site in Ahmedabad, fostering coalescence, and rebuilding the dismantled community organisation to foster social accountability systems. The experience has reinforced learnings from previous work that poorly planned relocation increases the risk of impoverishment and negatively impacts residents’ social relations, which severely affects their ability to come together to demand social accountability. As such, we had to innovate our engagement strategies to rebuild trust and confidence and strengthen community participation and organisation, which we share here.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.025 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.040 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".